DataCalc: Ad-hoc Analyses on Heterogeneous Data Sources
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Storing and processing data at different locations using a heterogeneous set of formats and data managements systems is state-of-the-art in many organizations. However, data analyses can often provide better insight when data from several sources is integrated into a combined perspective. In this paper we present an overview of our data integration system DataCalc. DataCalc is an extensible integration platform that executes adhoc analytical queries on a set of heterogeneous data processors. Our novel platform uses an expressive function shipping interface that promotes local computation and reduces data movement between processors. In this paper, we provide a discussion of the overall architecture and the main components of DataCalc. Moreover, we discuss the cost of integrating additional processors and evaluate the overall performance of the platform.
Details
Original language | English |
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Title of host publication | Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 |
Editors | Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye |
Publisher | IEEE, New York [u. a.] |
Pages | 463-468 |
Number of pages | 6 |
ISBN (electronic) | 9781728108582 |
Publication status | Published - Dec 2019 |
Peer-reviewed | Yes |
Publication series
Series | 2019 IEEE International Conference on Big Data (Big Data) |
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Conference
Title | 2019 IEEE International Conference on Big Data, Big Data 2019 |
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Duration | 9 - 12 December 2019 |
City | Los Angeles |
Country | United States of America |
External IDs
Scopus | 85081362423 |
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ORCID | /0000-0001-8107-2775/work/142253464 |